CBAM Integrated Attention Driven Model For Betel Leaf Diseases Classification With Explainable AI

• URL: http://arxiv.org/abs/2509.26484v1
• Date: Tue, 30 Sep 2025 16:30:09 GMT
• Title: CBAM Integrated Attention Driven Model For Betel Leaf Diseases Classification With Explainable AI
• Authors: Sumaiya Tabassum, Md. Faysal Ahamed,
• Abstract summary: This paper presents a lightweight CBAM-CNN model with just 2.13 million parameters (8.13 MB)<n>The model's capacity to discern minute variations among leaf disease classes is improved by the integrated attention mechanism.<n>The proposed model achieved a precision of 97%, recall of 94%, and F1 score of 95%, and 95.58% accuracy on the test set.
• Score: 0.48342038441006796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Betel leaf is an important crop because of its economic advantages and widespread use. Its betel vines are susceptible to a number of illnesses that are commonly referred to as betel leaf disease. Plant diseases are the largest threat to the food supply's security, and they are challenging to identify in time to stop possible financial damage. Interestingly, artificial intelligence can leave a big mark on the betel leaf industry since it helps with output growth by forecasting sickness. This paper presents a lightweight CBAM-CNN model with just 2.13 million parameters (8.13 MB), incorporating CBAM (Convolutional Block Attention Module) to improve feature emphasis without depending on heavy pre-trained networks. The model's capacity to discern minute variations among leaf disease classes is improved by the integrated attention mechanism, which allows it to adaptively focus on significant spatial and channel-wise information. In order to ensure class balance and diversity for efficient model training and validation, this work makes use of an enriched dataset of 10,185 images divided into three categories: Healthy Leaf, Leaf Rot, and Leaf Spot. The proposed model achieved a precision of 97%, recall of 94%, and F1 score of 95%, and 95.58% accuracy on the test set demonstrating strong and balanced classification performance outperforming traditional pre trained CNN models. The model's focus regions were visualized and interpreted using Grad-CAM (Gradient-weighted Class Activation Mapping), an explainable AI technique.

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